{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "colored-precipitation",
   "metadata": {},
   "source": [
    "<center><h1>第六次作业</h1></center>\n",
    "<center>3018233061 樊一飞</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "established-thickness",
   "metadata": {},
   "outputs": [],
   "source": [
    "Needs[\"HypothesisTesting`\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "traditional-still",
   "metadata": {},
   "source": [
    "## 1.\t设随机变量X服从B(20, 0.4), 请计算\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "athletic-tragedy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "BinomialDistribution[20, 0.4]"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = BinomialDistribution[20,.4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "taken-satisfaction",
   "metadata": {},
   "source": [
    "(1) $P(X=0)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "subject-joyce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#48;&#46;&#48;&#48;&#48;&#48;&#51;&#54;&#53;&#54;&#49;&#54;</pre></div>"
      ],
      "text/plain": [
       "0.0000365616"
      ]
     },
     "execution_count": 167,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PDF[B,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "formal-wiring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "Probability[pred, x  dist] gives the probability for an event that satisfies the\\\n",
       " \n",
       ">     predicate pred under the assumption that x\n",
       " \n",
       ">      follows the probability distribution dist\n",
       " \n",
       ">      .Probability[pred, x  data] gives the probability for an event that satisfies\\\n",
       "\n",
       " \n",
       ">         the predicate pred under the assumption that x\n",
       " \n",
       ">          follows the probability distribution given by data\n",
       " \n",
       ">          .Probability[pred, {x , x , …}  dist]\n",
       "                                1   2\n",
       " \n",
       ">            gives the probability that an event satisfies pred\n",
       " \n",
       ">             under the assumption that {x , x , …}\n",
       "                                          1   2\n",
       " \n",
       ">              follows the multivariate distribution dist\n",
       " \n",
       ">              .Probability[pred, {x   dist , x   dist , …}]\n",
       "                                    1       1   2       2\n",
       " \n",
       ">                gives the probability that an event satisfies pred\n",
       " \n",
       ">                 under the assumption that x\n",
       "                                             1\n",
       " \n",
       ">                 , x , … are independent and follow the distributions dist\n",
       "                     2                                                     1\n",
       " \n",
       ">                   , dist , ….Probability[pred   pred , …]\n",
       "                          2                    1       2\n",
       " \n",
       ">                      gives the conditional probability of pred  given pred .\n",
       "                                                                1           2\n",
       "\n",
       "\n",
       "Attributes[Probability]={Protected, ReadProtected}"
      ]
     },
     "execution_count": 164,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?Probability"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "divided-drain",
   "metadata": {},
   "source": [
    "(2)$P(X=1)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "religious-constitutional",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#48;&#46;&#48;&#48;&#48;&#52;&#56;&#55;&#52;&#56;&#56;</pre></div>"
      ],
      "text/plain": [
       "0.000487488"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PDF[B,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bulgarian-marshall",
   "metadata": {},
   "source": [
    "(3)$P(X<2)=P(X\\leq 1)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "dietary-assessment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#48;&#46;&#48;&#48;&#48;&#53;&#50;&#52;&#48;&#52;&#57;</pre></div>"
      ],
      "text/plain": [
       "0.000524049"
      ]
     },
     "execution_count": 130,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CDF[B,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "municipal-gateway",
   "metadata": {},
   "source": [
    "(4)$P(X\\leq 6)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dutch-hamburg",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#48;&#46;&#50;&#53;&#48;&#48;&#49;&#49;</pre></div>"
      ],
      "text/plain": [
       "0.250011"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CDF[B,6]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "radio-anime",
   "metadata": {},
   "source": [
    "(5)$P(X>10)$\n",
    "$=1-P(X\\leq 10)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aerial-fraud",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#48;&#46;&#49;&#50;&#55;&#53;&#50;&#49;</pre></div>"
      ],
      "text/plain": [
       "0.127521"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-CDF[B,10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "invalid-construction",
   "metadata": {},
   "source": [
    "(6)$P(X\\geq 15)=1-P(X<15)=1-P(X\\leq 14)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "institutional-tribe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#48;&#46;&#48;&#48;&#49;&#54;&#49;&#49;&#53;&#50;</pre></div>"
      ],
      "text/plain": [
       "0.00161152"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-CDF[B,14]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aging-chassis",
   "metadata": {},
   "source": [
    "## 2.\t给出20个服从均值为0、标准差为3的正态分布的随机数组成的表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "diagnostic-karma",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "NormalDistribution[0, 3]"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nd = NormalDistribution[0,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "suspected-titanium",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "-0.183571\n",
       "\n",
       "0.607508\n",
       "\n",
       "-0.104599\n",
       "\n",
       "1.14433\n",
       "\n",
       "4.47101\n",
       "\n",
       "-1.16602\n",
       "\n",
       "-2.52647\n",
       "\n",
       "-5.49995\n",
       "\n",
       "-0.931936\n",
       "\n",
       "-2.20432\n",
       "\n",
       "5.15612\n",
       "\n",
       "2.77129\n",
       "\n",
       "1.57564\n",
       "\n",
       "-0.80319\n",
       "\n",
       "-2.3381\n",
       "\n",
       "0.155548\n",
       "\n",
       "-1.28578\n",
       "\n",
       "0.265443\n",
       "\n",
       "-0.0885983\n",
       "\n",
       "1.94545"
      ]
     },
     "execution_count": 117,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RandomReal[nd,20]//MatrixForm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indirect-philip",
   "metadata": {},
   "source": [
    "## 3.\t给出4个样本值：\n",
    "{1.1, 2.0, 3.2}, {1.3, 2.2, 3.1}, {1.15, 2.05, 3.35}, {1.22, 2.31, 3.33}\n",
    "\n",
    "计算样本个数、均值和方差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "several-milan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "1.1    2.     3.2\n",
       "\n",
       "1.3    2.2    3.1\n",
       "\n",
       "1.15   2.05   3.35\n",
       "\n",
       "1.22   2.31   3.33"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {{1.1, 2.0, 3.2}, {1.3, 2.2, 3.1}, {1.15, 2.05, 3.35}, {1.22, 2.31, 3.33}};\n",
    "data//MatrixForm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "structural-upgrade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#52;</pre></div>"
      ],
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Length[data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "renewable-growing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#123;&#49;&#46;&#49;&#57;&#50;&#53;&#44;&#32;&#50;&#46;&#49;&#52;&#44;&#32;&#51;&#46;&#50;&#52;&#53;&#125;</pre></div>"
      ],
      "text/plain": [
       "{1.1925, 2.14, 3.245}"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mean[data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "sticky-amount",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#123;&#48;&#46;&#48;&#48;&#55;&#53;&#53;&#56;&#51;&#51;&#44;&#32;&#48;&#46;&#48;&#50;&#48;&#48;&#54;&#54;&#55;&#44;&#32;&#48;&#46;&#48;&#49;&#51;&#55;&#54;&#54;&#55;&#125;</pre></div>"
      ],
      "text/plain": [
       "{0.00755833, 0.0200667, 0.0137667}"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Variance[data]"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "israeli-dividend",
   "metadata": {},
   "source": [
    "## 4.\n",
    "![image.png](attachment:14486a6d-d2d4-401e-a77b-4f16d0389c7a.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "starting-thesaurus",
   "metadata": {},
   "source": [
    "(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "accessible-victor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "15.1\n",
       "\n",
       "14.8\n",
       "\n",
       "15.2\n",
       "\n",
       "14.9\n",
       "\n",
       "14.6\n",
       "\n",
       "15.1"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {15.1,14.8,15.2,14.9,14.6,15.1};\n",
    "data//MatrixForm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "domestic-acting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#123;&#49;&#52;&#46;&#55;&#49;&#51;&#44;&#32;&#49;&#53;&#46;&#49;&#56;&#55;&#125;</pre></div>"
      ],
      "text/plain": [
       "{14.713, 15.187}"
      ]
     },
     "execution_count": 48,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MeanCI[data,KnowVariance->0.06]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "searching-baking",
   "metadata": {},
   "source": [
    "(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bizarre-organization",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#123;&#49;&#52;&#46;&#55;&#49;&#51;&#44;&#32;&#49;&#53;&#46;&#49;&#56;&#55;&#125;</pre></div>"
      ],
      "text/plain": [
       "{14.713, 15.187}"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MeanCI[data]"
   ]
  },
  {
   "attachments": {
    "aa44c475-7912-4bc0-9614-a8ad5268c49f.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "correct-eligibility",
   "metadata": {},
   "source": [
    "## 5.\n",
    "![image.png](attachment:aa44c475-7912-4bc0-9614-a8ad5268c49f.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "stupid-speech",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#123;&#48;&#46;&#50;&#49;&#48;&#54;&#48;&#50;&#44;&#32;&#49;&#46;&#51;&#50;&#55;&#48;&#57;&#125;</pre></div>"
      ],
      "text/plain": [
       "{0.210602, 1.32709}"
      ]
     },
     "execution_count": 58,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FRatioCI[2.4/4.7,13-1,17-1,ConfidenceLevel->.9]"
   ]
  },
  {
   "attachments": {
    "f0e893c3-631b-4245-b906-2a2cd7e7655f.png": {
     "image/png": 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CcBrAcYEHj9ISkaKeAAfuYVrLNvoQHnKdvjNDzjqgMEixsgAjNhUCqAAcuu+KAbThuQOgANIuMVYom/HIxjoN6gAwpjyiaIxp0jAlE48A6vSf9U+mdKmnNbbH62rbb4GOH5upGg820VrWxvRqreoVPtEB5wbAP6DiekTkh3GOPAGCAX1McblEPrEWl8IzF3i497jH8AdQGX6njZvGH3sOeA54DtwoB25aeVo6v3aEPh07WSdSpGVLFmV6UgQv05QpoX1e+mSEQcXYQaSx6RK7zh4Fx3SNEVMQGFumFQx8YNxiPX2CUULpsXASJcn6B5Q/e6YYMMRumXh7AAMMB/Xmuq19sPLdPXnGM8LUh3UXLgE8MPgQwIOoA+DDBR4YfQMeeNjhBY14seHIEflhMOEHnj1gC2PpEoDFIkQ8ZgvfKdsIvmBYyQegQBQiTBhH2hQPbLnpebwSYx8GdwAt2hgmojdECyD6jHYwDQO5wANgCIjCmLOQlIgWANE2pq9YU0Aa+GePpjJG4i2CpC8MeDAu7DFkyiYyQJsBIoAjAIj7HheAnoFU6kB/UT714phoAnvSxDLgQQOlACAxRRSejrTrJ1aOUpMmrTUukTUdhzSlWVXlKv2iihasrJ5Td8niJIBD+p91LNcj2kHd4A3tJ+IB+GG9B3mwEQ2KtdbnRoEH0TbAKqDfk+eA54DnwLflwE27LkiX9iZo7tKV2nPshBrUrxeE4smYEDpKDQNsXidPLjCXbHPDGAJAQCzifsAITzewuBSlSOQCJYnCJCyNYWHagUWYePg2z8/0BPezBoR3iBDm5tg8ToAFnhj1YpGoPc0AQMGgEbEBtLjRl1h1BDiwPsPKdfcAF6IrRniTPJFi61Kog63r+O1vfxvJ4ze/+U1k7QYGN/xWU4AbBsElW9SHN/7zn/88MI6EvuEBfMP7J3KCYeQcoIJIFMS9gA0AF/1jhLfOVAbpjfCQqY/x0c6H90w58DKyMOiAB/A9FvBgESf8o770gxtxAESEgRAAgboYmLI6MFbo0zBgpO0YVbeP7PiWW26JgD3Lh3azYJipJ0CREfXkHNNiLm/sOnvGJTz/JgTYjQU+ziXu1Nb12xV5gv3UBg0eMECj5ixX2vtpFQA15CkrgGN1AlCxuJdpPEAB9wG4iCIRtQGQszE1yfRamIiMmByHr7n/AZrIIEDUk+eA54DnwLflQNRUy7Url7XRWRyHoYs1TYGx5gkAQq8YpljeOxVjeoAIBAYcgIBniTFz558xmqzMx3DisXHMxiI97keBW4jX1guQN1EHDBeEB21verTICpEAPFS8x6yIdQF4flauu8ewA7wg6s8jsywkZbElxNQJU1AYYhS43csUkNUHD9ie7glukoLFixhRl7gfQ8DUDFEY+ELUAONHhARjxpoNnrYhrA5wM4NNG/FwY3mkgA97jwVtYOrI+OaWHz6mjHDEhTQYNwCXu5jV7gVoEMVicSZTKbYWhetEclxjTF9j/OP1D9NEtMmmasgD8EKfGp/dPREcd62LpQf0xjK63EtUg6hMLKIf3Ed6Y6WJdw4+u/WOly7WeaI78RaouukZH0RuXIACYGPNkj26bunhsdsXdp51W27EzM77veeA54DnwPfJgSjgES7IwuTh8/znGp4tSjbWIkbSsNIeJWhAw96MGCu/WOe4n/xjEWAE7zseUScAyfWIPPDu45F5eSh6lDrG3tprgCh8L9fNkybvcP4YF8vD7iUyBK/C7YV3GA33vDtNwP20wXhs+dmeets1jvHyb4TipcPLZotFxku+UxLumzAf+B/LGLr50v82zcJ56ws3jR2TX7hMxigRnnjEtXhjPFzfeHn8K8/H66MbrRPj18bpjd7j03kOeA54DnxbDmQJPL5t5v5+zwHPAc8BzwHPAc8BzwGXAx54uNzwx54DngOeA54DngOeA98rBzzw+F7Z6zP3HPAc8BzwHPAc8BxwORAAD+bRmetm/QIbc8e8NdT+82RAeE2Cm4kd84KoG1kYZ+n93nPAc8BzwHPAc8Bz4P8vDgTAg0cHeZSVJ1DYeDsnr622/7zB0d5TwdMGPIrJeyp45TRviGSRIE9i8BQGj+7xSB9pslrYlzWbL2nPqmVBHstW7lTsJaJXdXz/IZ1OynhUNDrPqzq4cXmQx6q9aV8Xjb5u/67qwJq0dEtXbNOZFDvP/qL2rqUey7Uj1kdtIkmTdWTziqCslbtOKDVyPnyQokPrVwXpli7frBMZX06XdEWJG6y+7ofSM/JIOZuoVSuWpt2/dKmWr1qnTRs3aP+JSzHLPLZpTXpZG3Qk1pOQl0/o+PGTuhK/wjq9My2PFZsTMx4DzahS6OiazhxJ1Ln09cBBfZe79V2fVt+TsetrmV0+fVyHj5xR/KXDltLvPQc8BzwHPAd+bBwIgAcvneKtjLaSn3di8E4I+8+jrryvAuLRTt7oeOuttwbvauB9AWwAFb5rwaOpXOelXLzV9OtSasoJLR7UUoXvez7II/cj+fVyz5lKvBQNME4ndFOBv+dW7xUxjHTqFa2b3k7Fnn5GBQsW0ANF62rimmOKwhRBxc5ozdh2KnHfs0FZ+Z7Io9rvTtC+iylS8gnN/rSZCj6VSwULPquc5d/U/B0xylKqtn3VWaVyZA/yeKRgdQ1bfEDXMhnzC9r6eUeVfSBHkK7gUy+o6mvDtO0M5vWa1k59Q0Wfph4F9ECxlzRl3fFMYOLK+lGqXKZQ2v2Fiqng03/Vf/7nL9Vswh5Fc+eS9szvoUqP5FCBggVVMEdOlWvcX+uOOugj+aiGvvScSjf7RLujAFBGjx3b+Jmq5XsiKO/J3KXUZcoGwZp4tO6zRipRqKQmp7+C89K6EapYyqnvU3/Rz/7zZr06aW+MvkjPNWmrOhd+UkUbj1LsZ2file7Pew54DngOeA78GDgQAA9eZuW+EpvvNdjbSGkE7wEAULjEF2J5vBTiJVDf9GVLbp4cn1zUUQ/e+px6b0m3hgfG6MV/FFbPJUcyDPGJlXq/5qP6/X89rY/XZI6HnFk9UsUff0xtF6ddW9ertJ6s0kXbL0RbzUsbPlL2W7Pp3WXpr2869rmq3V9QXefv15GVnyjPQ7nVazMm/YKmNi6op14epRPRFl4Xd8xQhewPqsmstMdUdw6spuwvttWqk9H+esqeIXr+1ofUalb6451nFqjhY/nUdsI2Hds2QSWeeFztl6UBgzU9Sil7tW7amZQJvTjsuqhpb1bVC7Xf16ZToUodmagif7hfL43ekZY+abVaPpNHL/dZHhj8nV/2U+PKhXTHz/5DeVoN1v4YTyynnlqr5jn/R+UGbgzyODvrdeUsXEtTdmb+/sv5HV+o85s1df+t/6F/5i6tGTGfYL2gyW0q64W6XbT5dKi+1qqUJG2c2Ej//I/bVOzVccr4VrAl8HvPAc8BzwHPgR87BwLgwcui7FPzRDr4xsZdd90VRD34z8fQmHYxIiJClMPem8BLnXjT6HdB57cu0oiJa5QRW1inN54qog5T9qYDj6Oa0au92rRtpBKPV1C/FacyFXt291JNGbNE9saKlT1K67naPbXrYrQhv7J7mUaOW+F8nGuHOuYpoY4jlmnh4DbKV31gJI/kjYNUOPdbWhJy+ZMSV2nqqAU6ko5ptnxaTTkqd9DaUyHgkbhKo0Yv1sEI9jmgD4uXUIvei3R4z0pNHbtU1pLl3UvruTofas+l6Pq6DT217AMVfq68PtuY+aumOrpeY0ct1J7LVthxDaxYWg3enasL11I07/2X1LD7AH3QrJBefusj7YiRRerpzfp8xGztTsd1x6e3UZ6SL2nGnszvPNk7vKNefvdtvdvtDTWtVkaTMn8iRicXd1bB5ytqqAFKtzHpxyc2TtM7LRuqXtlyqtF4uPxH2GMwyZ/yHPAc8Bz4kXMgAB68bZLpFqZG2Pg+B2/ftP98+I2vokJMv/CGTV6JzZQKb3fkzZy86TEr4q2aAJqePXtmlSx0LVmLP6qmRwo1VcL+NOu4Z/bHavNGf63ZPFMvPV1SHy0zcx26VdKplSPUuHAePfhoKX2SkBg/vB/cmqo1Qxro8QINNG/TAU15t4KebzM9MoWRtHmoKjxUWSPjzEtc2DJFrYrn0yMPF1G3WTuvuz5hy/hXlS1XBY1clQGxTiwfppcL5tYDj5dR/yUHMyI84aYl71O/2iVVuNVonY2PTSJ37ZnZXs88X0J9E47pWkqqLp2+EFzbNqCa6rXsqe0xgIfdnHp0qTpVyq9sD+RSy8FLFQoaBckuHz8T1DV510i9Wr6kJuyzu9P3yXvVp0YJFX19bNz6pl7crgEtmmnEsg0a36KSXmwwzAOPEBv9X88BzwHPgX8HDkSmWtzXY/NabnstOI3kiRebauE15nzbhE+8Dx8+PHhtONGO6wEPnna5+eabM32vIy4Tk45ozoeN9HzJ2hq1Lm1x6NUDy9T7/bc0mW9VJc9XncdLa8CGOAsUSHLuiLavW6cN0/uqYqlWmpyYZnAzlXnluBb2b6ncxSqr/4pTStU5jX69uHK0miLLPWnzEJV7sJyGbrcz0bmkXjiunevXauPsQapaoplGbj0ZGzgkn9byEe2Uv0hZffDlgSiAcu1sWn3XT/tYFUq11vRDzpoMp7ijiz5U+dxF1W9dnCmLSNpzWj+ps4oXKa63p+/U5RBIWfFRJdVr1StL4KGrZ7Vv01ptWDxOL5dtqB7zdsUFcCdWD1SzCpmBx5GEHiqbp7gGbIxX3ytaM6qd3hzJB+9SNLlZJVVqOsGJekUa5A88BzwHPAc8B37kHAiAB1+KZZ0HHyhj4yNufPvC/jOt4kY8WDjKNx74HgXf7uBbINcDHl+HT9eOrVXPRiWVv1Ibzd6bbqxST+vzbrWVo3ALjZk4WRP7t1LOO7PppZ6TtDsjaMAn03Ru3xYtXnPYMep71b3ovWo5zv3weHqNTm9V/1bllbdsM03eZosdzmt+76bK12hyJI/kLUNVMkcbzT8fbTyTDm7VopWJyggaHFffMveq8cCNkXsjbb+wRyPaV1feEg01ar2tlUjRub2btXjNESf9Ln1Q+F69NjHWd2YuaEHPxspf9gNlfO83UkLGweVETexaT/mL1NKApbGf6skKeKSc2KXFS/c4xj9Fkxo8oRodpkWmhDIKSzuKDTzOa363RipQvodizMAEN17d+pkKPVNSbftP0KQJg9WkwNN6ukATTd96JPLF1nBZ/r/ngOeA54DnwI+TAwHw4JPhfMiNj56x8fjsnXfeGfnPR8F4xNaIR2v5UBwfCmNahogHH/DKivheCE/E8AhuVnT11CZ9Wq+KqnaeIzPNaekvasmwN1S1SmVVrFBe5Ys9qztu+ZOeLNdUs6MscLJ2j2+rp56oqgRb4nBphspnz6sP56d9ot3KTzm3SyOb1lC5dpMUNs17p3dV/uy1NSP9luXvltOjNQfqUPTSDR2b00U5Himjzw15pCxU9Wdy6r1p+6MjHkkHNLV1bZVuMUxhOLFzzOt6Mnt1LbGIxIXpKps9n/osCtdKUtJu9WpeQkW7LbVmZN5fPay57zRU8YafKKsPmWcFPJK3DFfBB15Q/z2W/Q61KZZLjT9eHuoXuy7FBB4Xd6rHKyVUomf8fr+wYYJav1xVlStWUPnyJfTk3bfr9rufVPuJ6xQ75pNRpj/yHPAc8BzwHPhxcSAAHnw1FiBhxBdn+YqsEYtI+YS9EY/NAj7YswiVr7jytdGsiEdyb7rppuCLo1ml2zG8ln79kz+qYK3GeqlObdWuXUu16tTT8PAi0rOfq9K9hdR3fRoSOLNuvPqPHKWdZ6Tki/v1Vb/GqlellmrVqqWaVRuo5bBlOnEtVZf3LNKoUZ9q/SnpzKzm+vP/uU25qzZU/brpZdWqpYELDynl3A4NfruCcuctr1q1SihX/toasTjtcY3zW2do4PDPtOmklHr1mJYMflX1q6aVVatKPTX5dKEOXkrR1UNrNH5kHy0/JiYL344AACAASURBVF1d3F53/8ctylGunho4ZX08e5euXNyn+X1fVt30+tao1kCvjVihk8nSpd3zNWToJ1p1JI1rqYdWqFm5u9VwYvTSy+TjWzVlZE8tBNWs76GHf3mzspWspYb16qTxsFYtdZ+yPsqQL+1eUlWbdNHWdNB0JXG5Rg7pqYREykrSpilv65UaVQMe1qpSQ/U+mKrNJ69IZ/dr5qgP9EVoVerxFZ+oQfH8Gh0BK1JK4lI1ffFuNZkc/XDstWObNH5IV83ZHR1Botwx9UupVP0xkU/FZzVe/DXPAc8BzwHPgR8XBwLgEa5y//79g8/eh8/bfwAHj9ASwWCqhc9zX494yRjrQq73ZtMzu5ZqyuSxGv7ZwODz6nxifeCgz7Rkd2h9xqXDWrNgufacTpseSdq/UvOXLtNRC5Mkn9Ticel5TF4lu/vqoS/UqVM7jV93RVcSV2v61HEaMXhQRlkDB2rBtvRIw9XDShj7mQYOHKzZ2zLmcy4fXq8FSxJ00DLVOa2YlF7W+CU6lR5pST6xWB9+0FqDl15S8pH1mjl9nEYOiS7ry41H0yIjyce1aGx6HlNWRQDC1WNbtWjRV9qb/sRv6uUz2rVxkXaBShxKObNeA3q+qj7zL0rHt2jOzPEaOZS6Z/Bx1poDzpSQdHrPaq3fvDuyYPTaqd1auugLbY+s103R5lnD0vIYMUf77Mnli7s0uk9TdZ4eHUG6cma/Nq1aocPWB0x8XTqjnRsWabcxJb3OKecOauWiz7XpmIWlrDGXlbhhlVZtcKev7Jrfew54DngOeA782DkQE3iMGDFCTZs2jds23l4KsedR3G/+htK4RXxvF5L3r9LMGVO0NvoVpd9LeSlHNuvL6eO1LPzyj++htNQTu5UwbZQWHo0GJN9DUdK5Q1o+bZjmHgzNO30vhflMPQc8BzwHPAf+nTgQE3hcvXo1eGz2eg3lGy9EMn5MlHrtmq5dvRq9/uL7akByclBW2Kf/XoqjrCtX4j5x8p2WmZLyw5X1nVbcZ+Y54DngOeA58K/mQEzg8a+ulC/fc8BzwHPAc8BzwHPg35MDHnj8e/arb5XngOeA54DngOfA/0oOeODxv7JbfKU8BzwHPAc8BzwH/j05EAAP1mnwIbhYGx+CO336dPD20uuxoG3bttd9auV6efjrngOeA54DngOeA54D/74cCIBHv379gm+v8P2V8MZbSn/7298GLwqDDXv37lVCQoLWrVunxYsXa8eOHTp79qx4TweP1/Iiss8//zxIc+BAjDeFOry8eGyfTlyIfgrj9Lb1WpSQoEWrtup47LeTBzlc2L9VSxMSlJCwVFv3R55rdXKPPrx0fJ+On7M3k6ZdSz6xTcsXkcdibTkQ42Vd6VlcObxLKxYnKGHxKu1Of6w1Ove0fylnE7WGdAkJWrUx+r0Vycd3BmUtWrFJJ7NYbXr1xB6tDOq0SOsynmuNVVxw7trpAzp6+mL0otLzB7R2KfVYpLXb018A4uaQekXH92wI6kldSbdi9Xpt2rpdJzJ/A8690x97DngOeA54DngOfCsOBMAjOTlZPKES3sh54sSJqlKlinjzKAS4KFSokG655ZbgNep89K179+665557VKJECeXPnz+4Xrhw4aiXjoVreXbtYNV6/p96d24GaDi6dZgqZMuuvIULK/cL+VW/5+cZ7+VwMjixeZJa5s+rFwoWVuGCLyhv/haauOlotPF10iftmKiGue5UmykZL926cnS1PmtfUkUKFVbhwjmUM19DDVyVGPkonN1+9ehCtS7+nJ7OVViF8z2v5xr21Npj0QCGtElHVuujOqWUMyf5FdTzj1VWr4W7gvdmXNy/VG/VzKs8+Qsrb+4cqv32NCXGeCXnuX0L1al8EeXKQx55lfPpOhqwYl/kezFWp8j+6DK9WfAe1eu7SoYXUk5t1eSeVVWyMHnkVs5nK6rz7J1R7+9Q6iktHPSa6CO2IsWL6/E7/o9ufqSCvsxgUaQYf+A54DngOeA54DnwXXEgyzUe06ZNC75aa6DDLTRbtmw6ciTNm+ZlYp07d3YvO8ep2jznE41blv7J0tObNbJ3S+W598+6/a9/VMeF9q7x4+pX9n4913xi2r3bhurFMiXVe3nIQl/eorbPP6xn6o9If8nWBY1tWkaFyvVT2ntFnaLP7dKkvm+o8MN36fbf/0pvzLQXXp3UhCZ5Ve6dCel5JGnh4E/14cTQ91VSjmnMK6X1SJVOSvtkzC51qvScCrWYLMspKC31tCa3KKS/Z2upjemYZPOIZsr52GvafO28pjfOq8cq91AQAzk6TbVy5tMrk4PXg2ZUNuWIBlR7TvcV6CT7uOuinnWU69m3lOnbapePav6IjqqQ81H9+Vf/V3UGb0oHJ+e18N0KKvly10ge26YNVaeBSyIvUMso0Dk6tVYNCr6gpmPXOt+Lca77Q88BzwHPAc8Bz4HviANxgQevSK9Zs2aw7iNcVu/evYPP2/O+D6hly5bB91rC6dL+p2rHwqGavibN0CYtnqQObzTUW4OHqF3F7Oo6z94DclKDqj6qHE1HB975lfUDVe7Fsuq32nz59NwvJ+qrSXO1dJ/dl6IVfZrpxaIdtSVUgcurZ6vr6/XUduBIvVPpCXWYkf6VlDNzVKNoDXX5IvzVlFAGR+aoWr6iajp2d+TCroltVDxbbc2zt3hyJfWsNn05R9NXZOR3akVfVcjWRCsTV6t1pWpqO3ZbekTmlCZ1aKYKLadG3k4aZJ5yUitnztacjccjZe37vLPKPtlECYbN0q9c3b1WA9+opdZ9x6tLjWxqNXhFWl6X1+r1KlXV6FO+8nqjdFlftSugZ2sOz/S9mhvNwafzHPAc8BzwHPAcuFEOxAQefJUW0BHrjaRJSUl67rnngi1v3rzq1KmT3n777Rv+Ou3V0+fTw/4H1adadnWck7Fo4uSOZerYMKdy5smj/KUqqPfMNTqReVYjqm0Xd05WtfzPqG7/1ZHpBktw7ewFXQ6WkJzT4NrZ1daiDJsGq0jpWmrzXmtVKpNHefIUUpOOs5TpBaNnlqpp/kKq2HuFZanV/SvqqScqavaxyKnMB0lb1anas8r/5he6uGuyXixdSZ3m2RzGGc1sV1elK3wk55MmmfJIOblMr5V9WsXfnqVz9vG49FQpF5KUlJS2NmZGi+f0Sr8laR9uOzRLtSpVVt0331LDavmUJ08eVWsyQnuyesHozjEq8nhxfbgmqwZlqp4/4TngOeA54DngOfCNOJAJeLCmo3bt2jp+PMPzdnP++OOP9etf/1rTp0/XyJEjgy/Y8nXa9957z012/eOLm9SjSna9PzcdeFzbrX4NKqnuGwO1Yv16bZjzqeo1qqG+C47FWbuRrCPrJqrxi3lUqcdsncnKuKbuU9+a2dRuSvr0xvohKvjgbcrepL+WbV6v9etnqn2FSqreaZYuRBn5C/qyY3Xd/UBFffbVWq1bP1/vlPqT7spZWXPj2OnzB1erW4NCytPsEyUmpUq7xqhksbLq+KVFQ85q5lu1VKJcL+2Kw6XT+xP0Ts18KvLmcB27FFWh0B3nNe6VZ9Ws/9I00JU4V7WfvVV3VXhXX66lXQn65OUaKtFsuGK/Sf2yZrfJr/z1+ysxK/6FSvV/PQc8BzwHPAc8B74pB6KAx+TJk1W3bl1dvBhaV+HkfunSJfGky/r164NHZ/lIXIcOHb428EhN2hwNPHYOV55SzTRyu31h7JgG1Syuso1GKbONP6c1k95X6fxF9frwdddfl5C6Pxp4bBqgPAWrqveKjA+/7RrbSLVqN9dym8GJtPmqFnZ/VYXy5lehanXU9YP3Vb98fS2IWuRB4mTtWzNCdYvmU/V3J+qIRWrOzFP9cg3VZbat6TijaR2aq2LTCcpUlC5r+8L+qloov+r3mKOzWWGOoH4XIsAj4Nqhz1W5REk1n5T2LR2SnFzSXQ3KvajpFnCJtEvSlZVqlr+kWo/cGAfcuYn9seeA54DngOeA58C350AEePAIbIMGDYJHY2Nlu2fPHn3xxRfBpeLFi2v79u3avXt38GVaIh5vvPFGrNvinguAR+VsGRGP43NUNU9RvT7JJiA26s3CT6jCO3PkLqeQzmvjqI4qU+pVTc36ad2MsgEeNZ7IiHicXa821Uur/dSMmMOX71VW8bpdtStqPcVZrZ0xVQs3GEQ4qWGNS6vmm/NDdbqqg8tH6qWi1dV1Qfjx1cP6rFopFX1tjAIscjFBTQsXVN0ROzPqFxxd0u65/VS1aF31W23lhZJk+gvweCaIeARQ8fIe9WpWXg0+XhBJuW1ESxUu+YpWZsxoRa4lre6j58u+pJEbojkcSeAPPAc8BzwHPAc8B75jDkSAx/PPP6+lS5fGzZ7FpgUKFAiuFytWTLbdddddatOmjZYvXx7n3lStndRRg+btjPowW+rFjepc5n61n2VRhyRtnPCWSj9RQFVq1lSNYoVVsE5bJey9KCUnacnYDhq+6pR0OkG1Hvq1/vpoMdVrUFe1atUK1qO81ntcMF2wZ04PDflqU5qRtxql7tWHFe7TaxMyIgFHlg1RhwYVVa1mTdWsWU6lir+uIUt4LiZZqye9p0ELWFB6Ris+e1MFXyioyrVrq2blYqrSoptWHE0LReya21ufzV2vq0lH9GnV+/Tfv39S1RrVV+30OjV8o7OWH5OOr5+guqVyqUyVmqpUKpfKNuivTemfnt80vbMGzNspXdyp9wrfodvuzKWaL78UyaNJhz7anCQdX9JfA6cv1GmLpARtO6+R9R9Rg48WRZ5aOb1xmno1r6Sq1WlXRZUp0EjdZu4MokI7v+iufjNWKH15iPaNqadnK9XUgkyPAxnj/N5zwHPAc8BzwHPgu+VABHgQxZgxY0bwVVqmU8Ib6zl4TwfE/rXXXtOwYcMCo1+/fv0sapWqA2s/1+LtoQmT5PPas26xdp1wFxekaveXUzV0yBANGTFDG9MNvFKuaveq6Vq+76J08ZjWLJquCeNGpqUj7ZAhmvjVap1OlhJnt1Hj3gt00c1Wl7R//WLtOBr9hMzxdV9oZHD/GC3caHMnqdof1NfWuCRpQ8KUtLJGTdbGdMBAgw/Pe0uNe87WmXOXtGftHE2aNFbD0+tDnUZOnqW96cGLizsSNGboEA0dN0u7nWoc2jBLi+DNlfPatmKWJk0YrWFOHqOnz9OhK9K5FZ3UqOt47TecFnA8WYe3LA1efua+hu38riUaH+QxXDMXZ4SFjm2eo4Ub9+pK+hTOpaNbtGHbdp2NAjNZdKW/5DngOeA54DngOfAtORABHomJicFCUXtiJbx/9NFH9fLLLwfFkRbat2+fmjdvHvOR229Zr298+5F5wzR2zUFdve76iG9cROTGkwkjNXb5Ll38Aco6s3S8xi7eHICrSAX8geeA54DngOeA58CPjAMR4EG9+SYLLwWLtZ04cUKXL0ctgBDv8Th//n/X+oDkK1cyvX30++qTFMpK/QFQh6S0srJ41/r31Uifr+eA54DngOeA58B3yIEo4PEd5uuz8hzwHPAc8BzwHPAc8BzIxAEPPDKxxJ/wHPAc8BzwHPAc8Bz4vjjwrYFH6g801fB9MeDHnK/n/Y+59/7/rrsfu//+/f+/qY+pS3j7V/RAuA72/19Rl39lmVHAo1q1asGCUdZzHD16VNeuXdPJkyeDxaPx1nLMmzcveG06DLwe8aRH06ZNxeJUtkOHDomywh+hW7lypRo3bhzJrmHDhkF6TvAkTZ8+fSLX4h2sWrVK7du3F1/ejUV82M7eS8L1/v37x823VatW+vLLL4NsFi5cqDfffDOSJS9cO3s2xksy+IRLaqo+/PBDdenSRSkpWa/PWLBggdq2bRvJ90YOPvroI02aNOm6SfmiMO2DrB/pW95OSz8Y/+GVnaPfqTPrfqy/LG24r+kfXihnxGv0R4wYYX8j+3BejDErz8qI98ZcMvnggw80bty4SH7xDkaPHq133nknuEx+lStXjpc0cp7vDa1Zsyby/7s4YE0UbbS2uXv4G+ajlcn7cVq3bh2sobJzsfZ8voDF3danpCFfxnasvFevXh1ZIE5a7kMGrV7winEBbdq0KXhizfqH9Vx8QqFcuXJBm44dOxbcZ99rCm76Gj+8AZlvPtGGWDyirHPn0h4J46vZbhrWoKE3rN5u2q9RhUhSa4vl5+4PHz4clGUy7qbl2P5TB3jv3ku949HWrVv1+uuvR/jtpuvbt6969eoVnEI269WrF7nM04TLli2L/LeDl156SfSvER/t5BUI16MBAwYEL4C0fo+XHtlARr4ODRw4MKYeiJdHixYthN6OR/DT5W8snnPdbQs68qmnnlLOnDkzbXzolAckIF6aaXlv2LAh0Bm8u4pzr7zySvCWbrtu+pL7ePoTeXNlkOucC9ueihUrBp8aiVWXZ555JsquuDxAxiib9v67UAR47N27Vy+++GJgjP/xj3/of/7nf4JBgzHMnj27ihQpErQZBcDAx1Cy8Qgu7/fAANs59rzXA+VgNHjwYP3yl78Unc07Q2B+rly5gtevv//++5Ys2GPkeU/ImTNngjypC4OYfHlTapkyZSLGcvHixUF9GLBc5x5ebjZr1iyVLl1alSpVCs6TzgYkBpB83HePdOzYMfhEPHkkJCRo27ZtkTpRF14RTxmAjmeffTbIk3rec889mjlzZiSte4AC6datm8gb8BEmyqA8NvLNkSNH5L+dtz0KDnJ5P2HCBD399NMBqLB07OkjCKEAKFWpUkUVKlQI/ufPn18I8JQpU4K60xe0AcGh3byXxc7Nnz9fPM3Ef9sYGxgrl+jLRYsWRU6hKLt27Rr5bwc9e/aM5EN+999/f6QOlj8CC+3cuTMTLwoXLiwe3XbbyrErkBgrAOdbb70V5IPhZOy6/YlRXrFiRSQfgO4jjzwSfOgwnDf8wzh+E+IbRrTR2ubu//a3v2nXrrQX2NFfVi59wPt0eD8N7bXztrd2bN68WV999VUwbhgDtIftiSeeCN4ijENgaa3utDNfvnz2N5Axk0fq9ve//128vRji0wm/+tWvdPfddwf3AIbgBe1hjPzzn/8MztNP34QAZRiyuXPnBrLo8oZjjAX6AerRo0egjyzNww8/rDvvvDPCVzftN6kLT+tZ3u6e8pH12267LQLcecmipbn33nsDPvCfOvzlL38JDItdX7duXSCv9KfpJ4wS/T579uxALkuVKhX0Mf1uhgrQ2a5duyAdAB6Zs/5HF6NPXFCDbkDP0UdGjRo1UvXq1YP70H0Y0TDhCOIkoJtiOWmuDJKOsWX1CO9NJ7hyhVPE05HIvZs+VlrGJv2Kw+CmtWNkEDBsvGWPsYbn6E07jy7CSBvVqVMnAFYAx/D25JNPBv1DWpxQywOZuOmmm5Q7d+7gLd0/+clPdMcddwR6hPx5YSbkyiB5uTLI27yRQQCmEfmuXbs2Uz2o19ChQwN5tLS2v3DhQsATq1tW79oK7rl0TIcPH9CF2P62ZZv1/sIhrV20QAsWLteBG1B9107v19GTF9If6kjWyT17dPBw1g+dRIAHxtwEBKMMYmYwQoASBhCEUUEZoczZMMpFixYNOsjOsWdAECmARo0aFSip8uXLiwFmBKoHTZrAYRCoAwadwYTxIC8En3eHUK/HHnssUDqWD3k+8MADuv3224O0gAkGcKFChYK68Xp38iAdxhVvCaDEddA+AxtPBe+DdpD2hRdeCMAWwsrAwQgArBjECB9Kj7oQIfrtb38bfNvG2sR+2rRpAky57UWRAOIAC0aAEsqjbQgRwIz/IGPOcWwbIADi4312DkDxi1/8IgBzKB47b7zBW6N/UFwoSdA/kSQAGFEj+hI0jXIlLdEEwCfn4BtgEUPlerUI3pYt0d8BJq0rEJSBsgkT/UxetqHw4Kv9Z29j4ZNPPom0h3aVLVs2GFMPPvigUNbWVvbw24ixQ/+5BCii3kaMM/rS8mCs3nfffUH/Ex2x8+wZE6Yo7f4b3VMGfey2z46pj/UTdbYy+bDfrbfeGlOmSEN+EMaH/qcM9gACxn3VqlWDc7TD0s6ZM0effvppII/ICseAc0Ao8mJ1YpwDlgHZeNY4FeSDJw3osPf1jBkzJiiHKFesyMr1+IM+2bhxY5CMqBrj0epge4wexhzCE8Yg2TXeKcQYsP+W9npRxXj1ilU+52gbBgS5tbxtfJIXMm5vbAbgorNcAlyhIwDvf/jDH4I+ht8AReSKdyeZfipZsmQANAAogHbGOyCcPvrd734X6Bt0DgYQXYHOgvDYASPIP84ZYwpZJrJs+gz5dmUC0IN+Rz8tWbIkyId2AnjgrZHJIPUEhP385z8P2oB+CssgYwYCxNlYZuzccssteuihh4JxZOfRuRCyzzkcRIAsxps2uht2iTQ4F/SH9Tn7AwcOBPfgbLjn3TGJfUE+MOzhDVkAHEL0L3kYEV1F7hnj6EEAA6AcG2JjICyD5IcMmlzSfvIxot+QyXA9+E/UGEfDJRx3+oloNXXDqaYMV9+56XXtpL4Y0ECv9hqivc67oqLSXOfP1RNb9XGrMsqTo7CK5H9SxRv21dbjUS/Fis7h2Eq1K3SPan20NP0llklaPrC9GjYYpG3n4kf5A+CB4sMDI8SEoUW5Y2BhGlMbDAQ8Qgjj5Ib0omuR8Q9lbwzq3r17cIGOZCCg1AgR48m4RIeC7AgRUh/zvlDGDGiUDwJLh7uEsCDgEIMQQTNF4abjGOHEEDdp0iQY+P/93/8dKFF3agAPAsNP/VB6CDEKAMIzgT/UBWCGwbIwG94b6WkbbUZYuNdCo/CTD/CRBqVuRJiP/GwagQgKgyxM/fr1izpFvqBvynfrj7cKGjcC4KFEMEo7duwITjMFhFcEoQipK18lxvhAKD4EDuUFAXxQTL///e+jPCuu3SjwCDJyftw6OKdjHuIlMnaog4XgwwlR9PAeQXaJtuGpEuWJRYwFm76LlybWfdc7R/ibaa5YhJIx4OFep9+RgRshC5NjqHAM6EfGA0bE5QF9T11QiH/961+DYwwOYxwQYNSsWbMAnDINAi8hvDjC7Ch/F4AxfpGFb0JMsaCUs6L9+/dH+EBkhHuM8E5N3jnH+AfIxZN5u+/r7gHG5t269zJW0GEADYwlx/AX5wdni0iXC86pL8YVoo7op1jyDa8BIOSPvsXxweBglNEPbOgbIhhGfJwTYAPYR75x0gCjpjtJR6SAviZ/dA+Ahnah55AXQCXEOMGx4Tq6wAgQRsTDQAnAxaKqloY9gNY13owbdCQePXbEiM9zuDaEKBD2hggDbYR/bIzrrCJqGGZkhfrFI8YO/KZ/whv9ZQDY7qeNOKHUAweVKCD1AHwSlSaK7BL2BHkjDXWJJ4PcYwAxXA/+o4uxe4wV4w3OHHbQjSbffPPNgePv1sGOTywYoXqVW2vKngwAZddubH9Jy7rX0CO5Gmpx8A2OLXq/ZE4V77Ey87e8rhzT/OEdVCHXY7r95v+rGgPWOl+HP6Khbeuq7ZCv4kZebkLR0mgECFSHwGCYGYSg6alTpwbea3g6gQ5lvpcNhYYisf9492EChaMsEFIGMRtGzjwG0iOUAAuUNajaCKWKwGBAMXLuGgsGJqDEpk0MeMQzTgg1YMCISEDYOCDwCCrKDkNH/oAKCH6B4KkL6BWlYEKIMqddCDPeIe1lmghjjUfCgEYISUMYzggDZN4d5xAUwuhhQrAxvhBgEc8YYYWPhE0hBAcjC18Y0EyxgJLZ/vjHPwaeC0oHz5a+QHBA1YQyMSYYJvoRz4rojAEPhIH2AnDMKAUFShHP2f6jBOFPLAJ82RQDURHSXo9QDowxwBTGDiDsTuNxP2OHfnUVnJsvSpb+cCNOXKdf4SlhagwYHst3ZcAYsxgFkwt3T1+4YVirKyDpRoHH8OHDA3CM90kkkD4F8LKxziVMlE8EzIhyANRWL6ZQDMDCL2SaqBiGDK8bsMQ5NvNaLa+vswdAky+EE8EYtTrQzzhARBCMD4xXZM7SWIjd/jNGH3/88W8UfYlXb+pl03VuGuSXCBkyjC4C8HAM4MBIcExf2NQqYX/0q4EFnCsAC/0cJvQAbSIPAzwAMKZCkRVkisgWxsmIOtpUNXJJNNZ1akgH4ECfwVfyBligIwAqjE++No5+4jx6kzRMVRuhS8zx5BzRE5ybMDEe8dwhjCZjBkPM+KHPkSumX9BXgCEI4Esd4CWAFx1jG3owli0JbuSDFmfOZIoy2TXbE1UZNGiQ/Y3aAyzcSC0XidxQPjLEuES/ohPol1iEvOCgEpmhHVnJILwwJzVWXpxj/NvYgUdM+bggG+CBzcpESYnq376hGnVZmfFpktTDmtn9VZUrX1FdRq/Rhcu7NG7KYm2Y2lsV0m23yVDV+t207uBO9WteS1Xfn5/+yZEUbR7dQQXL9lX4G6PX9m7QZ21rq3W/iepaK7taDFgU9dHTpAVtlbN6ey0+Gnud0014wAxulAmGE5TKNMT1CEECVTMAUV4oP47ZXKWEYOTNmzcwFgxm12vCi8aYotCIanAf9cBbMw+BeiA0eAJ44Qita9QQQjqDNCgqDDYDCgURixgYdCSGCwNoU0goakAJigFhILxIWuqPMJmggT5NCQKOUDJhFIyRpq1seBMYS6abMP5hQERYkMGPEqAOCAlKwBQVwuy2FzCEkkOh0XY8BeoN4UnAP6aTIMAiwJFzCDt5YmhpA0IOgGMe2EAliBtBo94od/oXEEqYFcVAaBZhdBdX4eHQfwAhCP4BJhBGjsMEwDJPmXYRAaIv2Fh7Y8TcJudsnQ6enbUT0Ea/odBJA/8BdYAa2kp9MQiMKTbaxPeEGOd4dFZXwCpGBLCFR8/YIjRMeu5jPBmgtXp9nb1F1Uwu3D2K3F2Qa/nCW+pjdbA2IF8G+ACLnEceGCuMK/qJ/rENRUgaAxKA45/+9Kf6zW9+ExgxeMZ1eGpjFR6QN7ID+CaiCQ2sgAAAE/tJREFUYhugBFkA2HEOXQHvY/WxtSXeHiMEKIYADPSb8YbxgSOAgjbggbeJfFga82LtP6FolLobYo9X9vXOYxzxdmlrLE+aKQ6rF+PXpqMZr3YeZwowD8EnpkPRX1xn7CIvsYAHuhd55F7GM4RxxfChazAS9LUbKaOuODr0BQADXQvhOOKE0B73PNcAINbn6Fz0AREO8sZBDBMGkLIBDRYVQddA9IEBH/7TdwB5dBD6CT1i15F7xqdF2cgXZwFgAkgPE6CDfoDgOwDT5IE9Y4XxjNy656mryQr8IUqEvjCZQjewAf6tLkxHkgdyRh+gJ7gHoIeOxcZhY+AvPCA6TfobkUF0OISeB/gjZ259cbbtHODVnAYi4dgP7BR6Fl1JRMsi6EGm6T8Xd05UneLPqf3CC2lnUq9qTo9XVavLxxrep7kK1m+j3h/11KBZy5W4Z72+SLdPNg7mzF+rE4nL9VqNkqr1Wdo0KN8t2zKqnYo921LLQvgh5eIlJaV/9GvWaznVpM+CKOChK6vUKm8V9Zl3MAMIORWOrPGAITAGdIsnTWcSUmNQoCRdIMD9rmHHgLF+IBYxD4fHSseSN4ONeUw2QA9CAADhPEYZ44NxNM+MgYFhBPET9mL6BmHhfrwiFBd15X4GFkCGAerWz60XAID5Vu5BWeFN0KkILusbEFaEnYFJfcgHY4sRRhHBI4w5aByvgfqgCMyLpyzagAdNe21jQGOMUegusUiMgQcvCHWjlFEyKCGiK3iGpgxoM//xlKkjAxGDTJ2oL9Eqwn4ockPWKAKEA9BBXWgnZOs9qCu8xsNFsQC4SIeAwQsMGEqBPXXHwLlhZLwkwBDKBmVK5AXFC1izKJHbXgSY+kJM5SD89AUb7TbjQRs4Rx6MIRQJSt6mQuABaw1Ig9IEVKBkWbuAIqa+jAk2jKUBTNqPcWc80i4ACUCUdIxDxhO85z/KIV4ExW1TvGP6ygx/OA08xyCEifGGASD6aPUH/KAAbV4fEME1xgyeOYqSNsF/24hWkgYZgRgDGEr2hIWRA2QaftoYBbQBPABMjAP+2wafMZiEizkHf5E36mBE3Rmj1od2Prx3gQfjzOpIOgwRdUD2zJADNHACjOgngLQR4AewSf9/G0IPEOLH2HEci+gD2s14ZJxTX6KFyBdyjKwClBi/EHxDf9IXGBeMFgYsFvCgTPqROhjwQD+ibzCElMsx8mNTBPQp4JlyMJD0D2Rr2dCVyApyZ0Q/IcvW7+zRcURZ3P609Dgd5EG70GFEHHHwAFH0gy3mpI2MeeSH/sMxQL4Yy9QPvQFfACLwkWgE9yK/rh4gLRvtIvIAoXu5nzLYsAvICdEBwI2dZ8+iasYCckI6dAORZyJBXEdeKNuuMX7Qp5YvOge5Jx36AIcMMICuh+/wFj1CepNBdMj1ZJB24GyQP/faBn/QR/zHboQdWe7DNhD9AMjGooNLuqnSo8U1Pv3TXGfWfqZyzzbRDHBI6lo1blFHBZuO1PlzSbpy/qh2OLaJ/t+157AuHl6uFlWLqMZAe0IxWVtGv6kizzTX4uiXljtVuKSJrz6nph8vjAYeOqVhtZ/SK4PWRH+wNf3OCPBA0TCg8dYRECNQJcaEvRGdiqEzI8qABGViqNhgokt4A0xJoGQQTncjYmCL1uweFK0JCmEvhOJnP/tZwHhCiShP8iB6ggBQdwivCG+M+iNcsQgFhuBgmBn0AAhLi1AAchAKBr4RoIHBQjvwXJiWYhCwYUAQTJSFEQoTYXDbieEjRMm9LuE54P3hhRPiZPAB9mg3Rt1VgIAKBjcE6sVw4B0DtMifdkGsncAAoKTgOygawcDbADjQfyh/FAPEvfCSNgLKqPef/vSnwJtB4SH8RFkAVxgdgIARfYNx5Rx8x4PGoFNWeBxwjws8UJooKvqCDYRvhGI38GTnMNQokTC5POIaHjCKxQhFSB8ZocQwGAg4hgA+Q4AwxrURPDNvxc4hpLQLxXo9woDTJyYX7p6oWayIBx5IWB6oL31koNHKpR5WR0Aj/cd44tiiSqTFUNOPKE7GDMAAnjEekWsbp8x5o2AxpMg8IN42/mOQkT87hyEwY48hBRiSF4Ay3HdWZ/Yu8ED+Ac/GG4yn3W/AA/0CKLU0gFycBvsP8KUdLuBxpzLdsuMdcy/eMXLs5hNOj4FChhkzjB34Rf34j4cPSDKQx714rKbL6Ff0D/oqFvCwsqiDTSejCwB8pm/QK+gcjCIEWKBM+M14cscv9UKfM0VpdeAe8qMN1u/sGV8AbdavMd6MAC6MHcYQ+gcAgsyiKzGGbj8DKAyEAJgAp0zZEoEmb/QoRP/DFzOwgBnyChN8iDfVgp5EbzLeLd/w/UyrorcAttTB9BbjGF0L8aQJTpcRPCRCgp5DttgDrjgGQIZ1DTJ3IzJo+aM/yNMl9E7YsXevc8yYYQzE0n9c35fQSzWz19dX6QGPue8/rxyvDVWAF87MVaPiZfTG8LSnffbP6hLV9/R/sUpva9W+rer9SgPV62OvFUjR1rHvqUiJ3srQ+OGaxQMeZzW80WOq3O2rtDqEbssEPFAmRAEYZCAxBAX07T6lgGAiAAY8EFg6BcPPxr0u4X0xsGMRxtFlOgMZg8S6CguJE23ASwC1cp56GSEQdj9KHi8Y4UNBxCKUhYV5KYt6GfCw9KBOztNOPBmmDhAqohPky8BFYQJOLPxv97JHYcbyHEDKli/pQNIItXmynGNQAhxogwmm5c0UFLylfHgO2qevEFqUAXOqCCteMAoCwIARArUjaCgp5gdB+PDBwpt4SShCBBUhggBYGBuUKCCH9SAoV7wEIwAikQ2UG8aKcYKgM08KGKGu4X5wgceNrPEA7Nm6DLwQ2sp4glBuCL5LeDn0L7w1AlC7wMPOswfgARAgvFX61AwPCicc8cB4oADccLebn3sMv+CtyYW7//Of/xwVObL7UPymIO0c45R6ucCDEDfAkX7GOJE3XiNyyTHjw4hxALCkzi4fMAbIuxGyZe97YcwT0WPPGGVjTABM7T+AzogybOww1x0rmmNpMTyMVwj+Y0SNNwBGDCXGwoAH66wYt5YG2cCg2X/qRTTU+o18kZF4UVirh+0BuRg/AFA8YjqCJyAwynjiZhBxVNA7JhfwBD2FfEH8t6lr+gu5gO/hPrZy0RtECZAjjBSEPqBfkWPk25UpwB4AA6IORH7C5DpyXMPQuH1n6ZE1AJ05AIwxeOyCJOQd0EEbXINNHuhedIyrnzDiyCNyy1oS009mO7iPMQdwRF+4G22xKSurI3t0PlOM8BCg4I5pNx3HjEnqhB4zfhKpQp8TaaFeyBf5UG/kGz0KT9GvRAfRncgTYxCQYIRupU+RIZNB5AMewjdXBuEN+SM7gHeObUPOcCDtP6AqTJTFmDPHM3z96MoPVTFbLg3akvY+rVlvPK48rUYH0xxrP66rUnXqa+yCAzp+KiuH6bzmvfuSXijzrtJGXqL6VMmn/B0TlAFFwyUDPHKoSaaIxxENLJ9b7cZsi/nttEzAA0WGEMN4M/AowzCZseI8QuICk3BaDEUsgSAd4S8GhRGIDuPEGgSmEvAoUDwYGbxoBgjG2cL1CBUKlwGGl44Xh8F0n3O3vNmTLx0NoXBiAQ/qgKBDKGPaRlSB6AqCByCizczz4Z2ZdxLckL74yp16sfMYRfhgCpK2uWteUGIMfNrKWg9TbnY/nib9YhvX6SsGM4PcVl9z3jwPgB2g0IAHIXuAB94iggTfEDCUDcYazxIC6MFHIhoYEbxMpjBchQFwgQ9GKGf4b8TctAm7ncPzMKN0vadaAL2MD7xgI8aGTbdgCGg7njyEUmDMIKQuMUZQtrEIvhgQBXigxKgffGFcGeixewFZRJEYC9cj+pLxEYuIDoWVP547ijkcTQHUMR4NeJAn7QwrIXhBxCQeobDNCJIG44G8G6FEkQ8jwso2l885DJDrOVs69qQFaAEiAL7u9ImbjmOAhxuRCl/nPzII8EBWiN64AIk20j8u0U8mV3aexz/h5/UIvhKByorQGbQLcnUZTlF4jGPYGFcQIApjRH0BS0RYkWOMfCxC5ugHwDB8QjaZFgMAI/dMf7t9D/Bgg+gfV/4sf6Kf9LURY9weI7Vz7KkTANeABzzFCTMi+kG/cS+6C2fEJRwj003s0UPoGHQK/QBgsfOuYwY/iDy499ImAGYYeBDJIj8zzjiG7phEtwEijb+MHcYm4950F+UAPABW2ACINnEevUl+6Db60Db0K7rTQB8yiGPiOo3kAwh1Zcb4Qx/CA/iH0+G2FT1BVNrOUd8wUVfsTFjmLd3VQwvUvGYBNZ+RNpV+YPn7ypvjPhWt00JtKtdS7vufUoEm07TzZFbAQ0rav1hv1SmogkWqqlr5XCpYoYOW7EvjUeKCj/XR+Nk6HjXtckljGz2ul3rOU9Qcw7EZqlSgkUasyXiXl9WVfQR4MBjpRLwiVzlhWOgk9hhzQk0gM4QQQ8QgJWyNsHHsbhaWAhnzZAcdiuFxN0ANgMKIAYmQoHjIkwHAIGLuHqGFMMwgXs6hvPGwEEwMJ+8Y4d54RDrqQh2YM6XD3cgCAxFlgdLDu8Hg4s0xGABINn3A6mPmoiEEygUJCBGo120nx4CcWCAOw0KYljxdJQaQYLDBUzf86baNvoo1UC0NRpqBDlhDYPHWCI9TdwQbflg/AXaog607AaVjfLmXehnoI3KDQGOwTYGgoBA615jCK9JgSKk/7YAHGG2OGVcIrztmOKY+bPQBdSI0bGnM8wQQQigEgCn9htdhoMTaT9+irNwxbdfY4yWaUQMEUj/aAl8wKq5ioQ0YA+qdlWGFv9QXI2ygzupvexQ8PKTe5MsYw3AzFlyiLwCsbsQDIEUfWDmWJyFnFKL9Z299S57wwTVAgBk8TRunyAXtNqLPUZSAVOpJ5Mtk0NK4exwM7idtVgS4YaxkRczTMx1gUzluWoyeRTnd8+4xIIRoJJHJ6xHKHP7CZ5d37rF5+uSF0xUGh5y3/sCQMEYgdBUyD1+QJyKU8eSV+82godsYPxY5AaQzviGcCQNujBu8ZfqQNuBkuMT4BzBw3QhwQhus320PoEJ/umOGexiTyB06F91oBH+Z5oFP8fQT/cS0SlaEniPyECZ0qq11oT/JhzqYvJIeBxS7Yn2FrWE6GSeCcxD30l76gnPYLQNNtNXV/6QHhDK1gd6zDRsFcLToOLJAGdbnVj7RN3SW/Wdv/GRP2a6OpLx4gDGofPoPtodIq+lb91pwnHxBsz9+VdVrD1Ka9buqHSsma+TY6dq87ZDWfzlX8zafyiJykZFj6rHN+nzkMA0bPlHr0l4XE1w8szNBC9du1YWo8EeKjm5dri37o/PeO7qeCjXvHvddHhHggSLAm0MoYDaEUicMhiLBUDHI+U94lHOgb4wDSJCFPhy7m82PmyfJwhg6zN2YVkFQITqRY/OI8UQstMWAZx2JEQYDQMKgsZAfSBfwER5Idg97lCgCTR2IXGCQDB1zHeWPciWMh7eNUTOU6YICBhB5QdQbRWGEsBENcdvJMQqDSILrmWFYEHwGtkUpLB/yxdDB03ieLH2VVdifaQ+iFpSPoQNgoiTwTsxTojw8X8Lf9BV8JJICcGA+2QVylAWYwEhbCJY2oAgxiGHCeIP04SXtQLGhBDhGwAlLu2OGYwQXpYYSRsEznWJp6C+OCTtDAAM2+o2ywkTIE+AS5m04Hf8xPPQPfI9FjC3KccdLrHTIEHXE26ONVnd3Dx8wrChAvBkUeKx88fgwKPQf3pxLgDY3TxQuhtY95wIJlLTJNvmwmBpPzMYpOsDGtJWDDNLPeODUF8X6bYk64VBkRcgQ7Y5FRLhMZ8S67p6zMeqeCx/DV8YuQNzlnXvMlAH9D8EDwvNhwihwD2Pa+IguAiRCRPCIaGCMYhFgCxm0KCgeuukK9C9TMEb2VlqiZPQNfcj4D/cPfYp+cad2MN7Iv/W77dHnyEoYONIXGGKb4rQ6sKcvabO9g8i9xjFj1KbMw9fsPxEPN2ps0S7sCtcgztEWVxdxHv4SmbG+YswAoNBTdo49T2XhDHIctlcWAbf6oAPgpfGFPdFCdKfZI0sLGHTLQQZxhN1zZgcph3zCRF8ii1kRYwgbYyAmVtrknV/o5erl9e6s3bpy/S+YxMriOziXovOH5qld/VYaPH9n5vd/pJcQAR4MNhQumw28MKpH2FCOJgzkgedH+ImNY3cz7408s2KYlUd+hijDHHDrFb5m/8P1tfPunnqQlxFG020P7bCQNoOa67GI8/FQvtue8L3ha5RNebEUmd0LT8P32TXOu+2x87anju51ay/n3bbx3wwfdYGXKORYbbRwo5VBG+L1G2nIC17SDo4BChwjxNznjhmOUcxWFyvDTQO/bGzZ9XCd7DygMaws7Fp4Tzvi8Zm0lJnVOLb8SEN9aRtlu3W3Y+MDaeBNvHzpC+6JReG8yZP2WhnsXSMXlqFwW2l/vHrAX/qNun5bCo+9WPnRtnAfWzrGpTt27fy33TOuXN65x/STAT/4Bq/CRLu4x03rpslKxknnyqB7n12L1ebwuXCfxhr/4TRuWeQXbhv9TpviEW2O11eUZXyLdz/tdscd6cmTMWf3ss+Kf25fURfrCzvPvda/5Mtm18L6IR5/YvHm68hgPB6G5TIen27k/OYpHdSx13Dti222biSLb5nmopb076ou3ebICZZkyjMCPDJd8Sc8BzwHPAc8BzwHPAc8B75jDnjg8R0z1GfnOeA54DngOeA54DkQnwMeeMTnjb/iOeA54DngOeA54DnwHXPAA4/vmKE+O88BzwHPAc8BzwHPgfgc+H/Ielq140jOxAAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "local-spell",
   "metadata": {},
   "source": [
    "## 7.\n",
    "![image.png](attachment:f0e893c3-631b-4245-b906-2a2cd7e7655f.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "isolated-relationship",
   "metadata": {},
   "outputs": [],
   "source": [
    "adat = {24.3,20.3,23.7,21.3,17.4};\n",
    "bdat = {18.2,16.9,20.2,16.7};"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dress-lithuania",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "{FullReport ->    MeanDiff   TestStat   Distribution           , \n",
       "                  3.4        2.15559    StudentTDistribution[7]\n",
       " \n",
       ">   TwoSidedPValue -> 0.0680496, Reject null hypothesis at significance level -> 0.1}"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MeanDifferenceTest[adat,bdat,0,EqualVariances->True,SignificanceLevel->0.1,TwoSided->True,FullReport->True]"
   ]
  },
  {
   "attachments": {
    "2512f1f5-0a7a-4707-8a7a-34e45cbb3097.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "frank-ordinary",
   "metadata": {},
   "source": [
    "## 8.\n",
    "![image.png](attachment:2512f1f5-0a7a-4707-8a7a-34e45cbb3097.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "dimensional-dancing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "-1     -1     -1     7.6\n",
       "\n",
       "-1     -1     1      10.3\n",
       "\n",
       "-1     1      -1     9.2\n",
       "\n",
       "-1     1      1      10.2\n",
       "\n",
       "1      -1     -1     8.4\n",
       "\n",
       "1      -1     1      11.1\n",
       "\n",
       "1      1      -1     9.8\n",
       "\n",
       "1      1      1      12.6"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    {-1,-1,-1,7.6},\n",
    "    {-1,-1,1,10.3},\n",
    "    {-1,1,-1,9.2},\n",
    "    {-1,1,1,10.2},\n",
    "    {1,-1,-1,8.4},\n",
    "    {1,-1,1,11.1},\n",
    "    {1,1,-1,9.8},\n",
    "    {1,1,1,12.6}\n",
    "};\n",
    "data//MatrixForm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "alpha-carol",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "FittedModel[Panel[9.9 + 0.575 x1 + 0.55 x2 + 1.15 x3, FrameMargins -> 5]]"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearModelFit[data,{1,x1,x2,x3},{x1,x2,x3},ConfidenceLevel->.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "international-pittsburgh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "Observed   Predicted   Standard Error   Confidence Interval\n",
       "\n",
       "7.6        7.625       0.414578         {6.74118, 8.50882}\n",
       "\n",
       "10.3       9.925       0.414578         {9.04118, 10.8088}\n",
       "\n",
       "9.2        8.725       0.414578         {7.84118, 9.60882}\n",
       "\n",
       "10.2       11.025      0.414578         {10.1412, 11.9088}\n",
       "\n",
       "8.4        8.775       0.414578         {7.89118, 9.65882}\n",
       "\n",
       "11.1       11.075      0.414578         {10.1912, 11.9588}\n",
       "\n",
       "9.8        9.875       0.414578         {8.99118, 10.7588}\n",
       "\n",
       "12.6       12.175      0.414578         {11.2912, 13.0588}"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm[\"MeanPredictionConfidenceIntervalTable\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "local-monster",
   "metadata": {},
   "source": [
    "## 10.\t某地某年高考后随机抽得15名男生、12名女生的物理考试成绩如下:\n",
    "  男生:49 48 47 53 51 43 39 57 56 46 42 44 55 44 40\n",
    "  \n",
    "  女生:46 40 47 51 43 36 43 38 48 54 48 34\n",
    "  \n",
    "从这27名学生的成绩能说明这个地区男女生的物理考试成绩不相上下吗?(显著性水平0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "generous-malta",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t.grid-container {\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t\tdisplay: inline-grid;\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t\tgrid-template-columns: auto;\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t}\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t</style>\n",
       "\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t<div><div class=\"grid-container\"><div class=\"grid-item\"><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#123;&#52;&#57;&#44;&#32;&#52;&#56;&#44;&#32;&#52;&#55;&#44;&#32;&#53;&#51;&#44;&#32;&#53;&#49;&#44;&#32;&#52;&#51;&#44;&#32;&#51;&#57;&#44;&#32;&#53;&#55;&#44;&#32;&#53;&#54;&#44;&#32;&#52;&#54;&#44;&#32;&#52;&#50;&#44;&#32;&#52;&#52;&#44;&#32;&#53;&#53;&#44;&#32;&#52;&#52;&#44;&#32;&#52;&#48;&#125;</pre></div><div class=\"grid-item\"><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#123;&#52;&#54;&#44;&#32;&#52;&#48;&#44;&#32;&#52;&#55;&#44;&#32;&#53;&#49;&#44;&#32;&#52;&#51;&#44;&#32;&#51;&#54;&#44;&#32;&#52;&#51;&#44;&#32;&#51;&#56;&#44;&#32;&#52;&#56;&#44;&#32;&#53;&#52;&#44;&#32;&#52;&#56;&#44;&#32;&#51;&#52;&#125;</pre></div></div></div>"
      ],
      "text/plain": [
       "{49, 48, 47, 53, 51, 43, 39, 57, 56, 46, 42, 44, 55, 44, 40}\n",
       "{46, 40, 47, 51, 43, 36, 43, 38, 48, 54, 48, 34}"
      ]
     },
     "execution_count": 63,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male = {49 ,48 ,47 ,53 ,51 ,43 ,39 ,57 ,56 ,46 ,42 ,44 ,55 ,44 ,40}\n",
    "female = {46 ,40 ,47 ,51 ,43 ,36 ,43 ,38 ,48 ,54 ,48 ,34}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "adolescent-geography",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "{FullReport ->    MeanDiff   TestStat   Distribution                     , \n",
       "                                                             73706072517\n",
       "                                        StudentTDistribution[-----------]\n",
       "                  3.6        1.55534                         3191547053\n",
       " \n",
       ">   TwoSidedPValue -> 0.133464, Fail to reject null hypothesis at significance level -> \n",
       " \n",
       ">    0.05}"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MeanDifferenceTest[male,female,0,SignificanceLevel->.05,TwoSided->True,FullReport->True]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "light-export",
   "metadata": {},
   "source": [
    "接受原假设，男女生的物理考试成绩不相上下"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "streaming-johnston",
   "metadata": {},
   "source": [
    "## 11.甲、乙两厂生产同一种电阻\n",
    "\n",
    "现从甲乙两厂的产品中分别随机抽取12个和10个样品,测得它们的电阻值后,计算出样本方差分别为1.40,4.38, 假设电阻值服从正态分布,在显著性水平0.1下,是否可以认为两厂生产的电阻值的方差相等.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "contrary-arena",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t.grid-container {\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t\tdisplay: inline-grid;\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t\tgrid-template-columns: auto;\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t}\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t</style>\n",
       "\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t<div><div class=\"grid-container\"><div class=\"grid-item\"><img alt=\"Output\" src=\"\"></div><div class=\"grid-item\"><img alt=\"Output\" src=\"\"></div></div></div>"
      ],
      "text/plain": [
       "TwoSidedPValue -> 0.0785523\n",
       "False"
      ]
     },
     "execution_count": 138,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = FRatioPValue[1.4/4.38,12-1,10-1,TwoSided->True]\n",
    "p[[2]]>0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "announced-movement",
   "metadata": {},
   "source": [
    "$PValue<\\alpha=0.1$,所以认为不相等"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "injured-device",
   "metadata": {},
   "source": [
    "## 12.为比较甲、乙两种安眠药的疗效\n",
    "将20名患者分成两组,每组10人,若服药后延长的睡眠时间分别服从正态分布,其数据为(单位:小时):\n",
    "\n",
    "甲:5.5 4.6 4.4 3.4 1.9 1.6 1.1 0.8 0.1,-0.1 \n",
    "\n",
    "乙:3.7 3.4 2.0 2.0 0.8 0.7,0,-0.1,-0.2,-1.6\n",
    "\n",
    "问在显著性水平0.05下两重要的疗效又无显著差别. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "arabic-power",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t.grid-container {\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t\tdisplay: inline-grid;\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t\tgrid-template-columns: auto;\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t\t}\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t</style>\n",
       "\n",
       "\t\t\t\t\t\t\t\t\t\t\t\t<div><div class=\"grid-container\"><div class=\"grid-item\"><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#123;&#53;&#46;&#53;&#44;&#32;&#52;&#46;&#54;&#44;&#32;&#52;&#46;&#52;&#44;&#32;&#51;&#46;&#52;&#44;&#32;&#49;&#46;&#57;&#44;&#32;&#49;&#46;&#54;&#44;&#32;&#49;&#46;&#49;&#44;&#32;&#48;&#46;&#56;&#44;&#32;&#48;&#46;&#49;&#44;&#32;&#45;&#48;&#46;&#49;&#125;</pre></div><div class=\"grid-item\"><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#123;&#51;&#46;&#55;&#44;&#32;&#51;&#46;&#52;&#44;&#32;&#50;&#46;&#44;&#32;&#50;&#46;&#44;&#32;&#48;&#46;&#56;&#44;&#32;&#48;&#46;&#55;&#44;&#32;&#48;&#44;&#32;&#45;&#48;&#46;&#49;&#44;&#32;&#45;&#48;&#46;&#50;&#44;&#32;&#45;&#49;&#46;&#54;&#125;</pre></div></div></div>"
      ],
      "text/plain": [
       "{5.5, 4.6, 4.4, 3.4, 1.9, 1.6, 1.1, 0.8, 0.1, -0.1}\n",
       "{3.7, 3.4, 2., 2., 0.8, 0.7, 0, -0.1, -0.2, -1.6}"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adat = {5.5 ,4.6 ,4.4 ,3.4 ,1.9 ,1.6, 1.1, 0.8, 0.1,-0.1 }\n",
    "bdat = {3.7, 3.4, 2.0, 2.0, 0.8, 0.7,0,-0.1,-0.2,-1.6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "unauthorized-preparation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "{FullReport ->    MeanDiff   TestStat   Distribution                 , \n",
       "                  1.26       1.52273    StudentTDistribution[17.4884]\n",
       " \n",
       ">   TwoSidedPValue -> 0.145703, Fail to reject null hypothesis at significance level -> \n",
       " \n",
       ">    0.05}"
      ]
     },
     "execution_count": 70,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MeanDifferenceTest[adat,bdat,0,SignificanceLevel->0.05,TwoSided->True,FullReport->True]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jewish-personal",
   "metadata": {},
   "source": [
    "接受原假设，两重要的疗效又无显著差别"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accomplished-password",
   "metadata": {},
   "source": [
    "## 13.\t绘制χ2分布在n分别为1，5，15时的分布密度函数图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "equipped-chess",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "-Graphics-"
      ]
     },
     "execution_count": 113,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Plot[Evaluate@Table[PDF[ChiSquareDistribution[n],x],{n,{1,5,15}}],{x,0,6]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "primary-miracle",
   "metadata": {},
   "source": [
    "## 14.\t一位美国数学家曾经做过一个实验：\n",
    "在一个人山人海的世界杯赛场上，他随机在某号看台上召唤22个球迷，请他们分别写下自己的出生月和日（如5月1日），结果发现其中两人生日相同。怎么会这么凑巧呢？请利用概率理论并借助于Mathematica来加以解释。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unknown-baseball",
   "metadata": {},
   "source": [
    "先求22个人生日不同的概率为：\n",
    "$$\n",
    "\\frac{22!\\times\\binom{365}{22}}{365^{22}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "sized-battery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#48;&#46;&#52;&#55;&#53;&#54;&#57;&#53;</pre></div>"
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      "text/plain": [
       "0.475695"
      ]
     },
     "execution_count": 148,
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   ],
   "source": [
    "1-22!*Binomial[365,22]/365^22//N"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlimited-strength",
   "metadata": {},
   "source": [
    "## 15.\t假设投掷一个均匀硬币只能出现正面和反面两种情况用Mathematica命令来验证投掷出现正面的概率为0.5。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "ultimate-apparatus",
   "metadata": {},
   "outputs": [],
   "source": [
    "CoinTest[n_]:=RandomInteger[1,n]//Mean//N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "younger-efficiency",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"\"></div>"
      ],
      "text/plain": [
       "-Graphics-"
      ]
     },
     "execution_count": 162,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinResult = Table[CoinTest[n],{n,10^3,10^4,100}];\n",
    "ListPlot[coinResult]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "precise-romania",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><pre style=\"&#102;&#111;&#110;&#116;&#45;&#102;&#97;&#109;&#105;&#108;&#121;&#58;&#32;&#34;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#32;&#78;&#101;&#119;&#34;&#44;&#67;&#111;&#117;&#114;&#105;&#101;&#114;&#44;&#109;&#111;&#110;&#111;&#115;&#112;&#97;&#99;&#101;&#59;\">&#48;&#46;&#53;</pre></div>"
      ],
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 160,
     "metadata": {
      "text/html": [],
      "text/plain": []
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     "output_type": "execute_result"
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   ],
   "source": [
    "CoinTest[10^4]"
   ]
  }
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